ArticleCurrent oncology (Toronto, Ont.)2023
Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.
Article in Current oncology (Toronto, Ont.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.Scientific reports · 2025Article
- Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives.Medical sciences (Basel, Switzerland) · 2025Review
- Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies.Diagnostic pathology · 2025Article
- The tooth transformer® revolution: autologous dentin biomaterials and platelet concentrates in oral regeneration. A parallel narrative systematic review.Frontiers in dental medicine · 2025Review
- Application of Digital Analysis for Assessment of Coronary Sub-Occlusions in Autopsy Pathology: It Is Time to Move beyond Histology Alone.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.